There are many descriptions of computer-aided searches of large search spaces, such as the world wide web, whereby narrowing the search space to a successively smaller and more precise area of interest is accomplished using one or more algorithms involving lexicons.
One problem with the use of lexicons is the limitation inherent in a pure textual search. For example, although a lexical search of the world wide web for matches to “blue sweater” might be refined through human-computer interactions to the more specific “blue sweater crew neck men's large”, the resulting search result set is likely to include citations for:                (A) Descriptions of an article of men's apparel known as a sweater and having elements of fashion known as a crew-neck and available in size large and extra-large.        (B) Descriptions of an article of men's apparel known as a sweater and having elements of fashion known as a crew-neck and available at large department stores.        (C) Many reprints and quotes from an often quoted article on the hardworking men on the crew of the Blue Man Group™ and their experiences during their tour of large cities.        
In the above case, the intended search scope is best characterized by the citation in item A. Item B is closer, however there was no semantic meaning to the keyword “large” to indicate that “large” should be used to modify the size of the article of apparel rather than to modify the size of the department store. Item C is wildly out of scope as compared to the buyer's intended search space, yet scores a hit (match) on the refined search terms.
Even more sophisticated computer-aided lexical searches employing lexical associations do not appreciably and consistently reduce the occurrences of search results returning citations that are wildly outside of the target scope (false hits). One commonly employed partial solution to the shortcomings of a pure lexical search is to inject lexical associations into the lexical refinements. Prior attempts to inject lexical associations into computer-aided searches have relied on the existence of a virtual expert advisor, or other access to a domain-specific knowledgebase. In practice such implementations merely inject lexical associations iteratively, resulting in the construction of longer and longer search strings. This technique can result in a rapid narrowing of the search space, however this technique does not reliably eliminate or reduce the occurrence of false hits or wildly out of scope citations.
It has been observed that when humans interact with computer-aided search engines (e.g., Google™, eBay™.com) in search of products, services or information, they frequently provide keywords that tend to be values or characteristics of the desired products, services or information. For example, when searching for an automobile, the keyword string might be:                “1997 Mustang red convertible”        
where each of the above keywords is the value of an implied attribute. A human would imply the following attributes;                Implied Attributes={Model_year, Model_name, Exterior_color, Body_style}        
Furthermore, a human would infer a mapping of the keywords to attributes as follows:                Mapping: {Model_year=1997, Model_name=Mustang, Exterior_color=red, Body_style=convertible}        
In use, a mapping between the human-specified values/characteristics and the correct corresponding attribute is required in order to enable an unambiguous and effective (i.e., few or no false hits) computer-aided search of a large structured data search space.
Thus, what is desired is a method and apparatus to confirm the mapping between the human-specified values/characteristics and the correct correspondence to characteristics found in an entity description (e.g. product, service, or information), among other techniques to overcome the above prior art problems (as well as other prior art problems not mentioned).